Conception sûre de systèmes de contrôle-commande matériels à base de COTS
Bibliographic record
Abstract
RÉSUMÉ. Cet article propose une méthode de conception sûre de systèmes de contrôle-commande embarqués à base de COTS ControlBuild. La méthode commence par l'élaboration des COTS ControlBuild, puis elle utilise la vérification formelle pour découvrir les erreurs de conception. Ensuite la technique de la synthèse du contrôleur discret (SCD) est employée pour corriger au-tomatiquement les erreurs détectées. La méthode proposée est illustrée sur un l'exemple d'un système ferroviaire d'accès voyageurs pour le train NAT. ABSTRACT. This paper proposes an approach for safe design of hardware embedded control sys-tems. The approach is based on a combination of formal verification and discrete controller synthesis techniques. Formal verification is solicited to detect design errors and provide coun-terexamples, while the Discrete Controller Synthesis technique is used to correct those error since it attempts to enforce previously verified specifications which do not hold. It automati-cally produces control code, which is correct by construction with respect to the specification to enforce. We illustrate the approach on a real train controller subsystem. MOTS-CLÉS : COTS, vérification formelle, synthèse de contrôleurs discrets, système à événements discrets, propriété de sûreté, propriété de vivacité.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".